AlphabetSoupCharity.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deliverable 1: Preprocessing the Data for a Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EIN</th>\n",
       "      <th>NAME</th>\n",
       "      <th>APPLICATION_TYPE</th>\n",
       "      <th>AFFILIATION</th>\n",
       "      <th>CLASSIFICATION</th>\n",
       "      <th>USE_CASE</th>\n",
       "      <th>ORGANIZATION</th>\n",
       "      <th>STATUS</th>\n",
       "      <th>INCOME_AMT</th>\n",
       "      <th>SPECIAL_CONSIDERATIONS</th>\n",
       "      <th>ASK_AMT</th>\n",
       "      <th>IS_SUCCESSFUL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10520599</td>\n",
       "      <td>BLUE KNIGHTS MOTORCYCLE CLUB</td>\n",
       "      <td>T10</td>\n",
       "      <td>Independent</td>\n",
       "      <td>C1000</td>\n",
       "      <td>ProductDev</td>\n",
       "      <td>Association</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>5000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10531628</td>\n",
       "      <td>AMERICAN CHESAPEAKE CLUB CHARITABLE TR</td>\n",
       "      <td>T3</td>\n",
       "      <td>Independent</td>\n",
       "      <td>C2000</td>\n",
       "      <td>Preservation</td>\n",
       "      <td>Co-operative</td>\n",
       "      <td>1</td>\n",
       "      <td>1-9999</td>\n",
       "      <td>N</td>\n",
       "      <td>108590</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10547893</td>\n",
       "      <td>ST CLOUD PROFESSIONAL FIREFIGHTERS</td>\n",
       "      <td>T5</td>\n",
       "      <td>CompanySponsored</td>\n",
       "      <td>C3000</td>\n",
       "      <td>ProductDev</td>\n",
       "      <td>Association</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>5000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10553066</td>\n",
       "      <td>SOUTHSIDE ATHLETIC ASSOCIATION</td>\n",
       "      <td>T3</td>\n",
       "      <td>CompanySponsored</td>\n",
       "      <td>C2000</td>\n",
       "      <td>Preservation</td>\n",
       "      <td>Trust</td>\n",
       "      <td>1</td>\n",
       "      <td>10000-24999</td>\n",
       "      <td>N</td>\n",
       "      <td>6692</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10556103</td>\n",
       "      <td>GENETIC RESEARCH INSTITUTE OF THE DESERT</td>\n",
       "      <td>T3</td>\n",
       "      <td>Independent</td>\n",
       "      <td>C1000</td>\n",
       "      <td>Heathcare</td>\n",
       "      <td>Trust</td>\n",
       "      <td>1</td>\n",
       "      <td>100000-499999</td>\n",
       "      <td>N</td>\n",
       "      <td>142590</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        EIN                                      NAME APPLICATION_TYPE  \\\n",
       "0  10520599              BLUE KNIGHTS MOTORCYCLE CLUB              T10   \n",
       "1  10531628    AMERICAN CHESAPEAKE CLUB CHARITABLE TR               T3   \n",
       "2  10547893        ST CLOUD PROFESSIONAL FIREFIGHTERS               T5   \n",
       "3  10553066            SOUTHSIDE ATHLETIC ASSOCIATION               T3   \n",
       "4  10556103  GENETIC RESEARCH INSTITUTE OF THE DESERT               T3   \n",
       "\n",
       "        AFFILIATION CLASSIFICATION      USE_CASE  ORGANIZATION  STATUS  \\\n",
       "0       Independent          C1000    ProductDev   Association       1   \n",
       "1       Independent          C2000  Preservation  Co-operative       1   \n",
       "2  CompanySponsored          C3000    ProductDev   Association       1   \n",
       "3  CompanySponsored          C2000  Preservation         Trust       1   \n",
       "4       Independent          C1000     Heathcare         Trust       1   \n",
       "\n",
       "      INCOME_AMT SPECIAL_CONSIDERATIONS  ASK_AMT  IS_SUCCESSFUL  \n",
       "0              0                      N     5000              1  \n",
       "1         1-9999                      N   108590              1  \n",
       "2              0                      N     5000              0  \n",
       "3    10000-24999                      N     6692              1  \n",
       "4  100000-499999                      N   142590              1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import our dependencies\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler,OneHotEncoder\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "\n",
    "#  Import and read the charity_data.csv.\n",
    "import pandas as pd \n",
    "application_df = pd.read_csv(\"Resources/charity_data.csv\")\n",
    "application_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kimac\\AppData\\Local\\Temp/ipykernel_27768/603693418.py:2: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
      "  application_df = application_df.drop([\"EIN\",\"NAME\"],1)\n"
     ]
    }
   ],
   "source": [
    "# Drop the non-beneficial ID columns, 'EIN' and 'NAME'.\n",
    "application_df = application_df.drop([\"EIN\",\"NAME\"],1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "APPLICATION_TYPE            17\n",
       "AFFILIATION                  6\n",
       "CLASSIFICATION              71\n",
       "USE_CASE                     5\n",
       "ORGANIZATION                 4\n",
       "STATUS                       2\n",
       "INCOME_AMT                   9\n",
       "SPECIAL_CONSIDERATIONS       2\n",
       "ASK_AMT                   8747\n",
       "IS_SUCCESSFUL                2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine the number of unique values in each column.\n",
    "application_df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "T3     27037\n",
       "T4      1542\n",
       "T6      1216\n",
       "T5      1173\n",
       "T19     1065\n",
       "T8       737\n",
       "T7       725\n",
       "T10      528\n",
       "T9       156\n",
       "T13       66\n",
       "T12       27\n",
       "T2        16\n",
       "T25        3\n",
       "T14        3\n",
       "T29        2\n",
       "T15        2\n",
       "T17        1\n",
       "Name: APPLICATION_TYPE, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look at APPLICATION_TYPE value counts for binning\n",
    "application_counts = application_df.APPLICATION_TYPE.value_counts()\n",
    "application_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the value counts of APPLICATION_TYPE\n",
    "application_counts.plot.density()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "T3       27037\n",
       "T4        1542\n",
       "T6        1216\n",
       "T5        1173\n",
       "T19       1065\n",
       "T8         737\n",
       "T7         725\n",
       "T10        528\n",
       "Other      276\n",
       "Name: APPLICATION_TYPE, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine which values to replace if counts are less than ...?\n",
    "replace_application = list(application_counts[application_counts < 500].index)\n",
    "\n",
    "# Replace in dataframe\n",
    "for app in replace_application:\n",
    "    application_df.APPLICATION_TYPE = application_df.APPLICATION_TYPE.replace(app,\"Other\")\n",
    "    \n",
    "# Check to make sure binning was successful\n",
    "application_df.APPLICATION_TYPE.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "C1000    17326\n",
       "C2000     6074\n",
       "C1200     4837\n",
       "C3000     1918\n",
       "C2100     1883\n",
       "         ...  \n",
       "C4120        1\n",
       "C8210        1\n",
       "C2561        1\n",
       "C4500        1\n",
       "C2150        1\n",
       "Name: CLASSIFICATION, Length: 71, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look at CLASSIFICATION value counts for binning\n",
    "class_counts = application_df.CLASSIFICATION.value_counts()\n",
    "class_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the value counts of CLASSIFICATION\n",
    "class_counts.plot.density()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "C1000    17326\n",
       "C2000     6074\n",
       "C1200     4837\n",
       "Other     2261\n",
       "C3000     1918\n",
       "C2100     1883\n",
       "Name: CLASSIFICATION, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine which values to replace if counts are less than ..?\n",
    "replace_class = list(class_counts[class_counts < 1000].index)\n",
    "\n",
    "# Replace in dataframe\n",
    "for cls in replace_class:\n",
    "    application_df.CLASSIFICATION = application_df.CLASSIFICATION.replace(cls,\"Other\")\n",
    "    \n",
    "# Check to make sure binning was successful\n",
    "application_df.CLASSIFICATION.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate our categorical variable lists\n",
    "application_cat = application_df.dtypes[application_df.dtypes == \"object\"].index.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kimac\\anaconda3\\envs\\ml\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>APPLICATION_TYPE_Other</th>\n",
       "      <th>APPLICATION_TYPE_T10</th>\n",
       "      <th>APPLICATION_TYPE_T19</th>\n",
       "      <th>APPLICATION_TYPE_T3</th>\n",
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       "      <th>APPLICATION_TYPE_T8</th>\n",
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       "      <th>INCOME_AMT_1-9999</th>\n",
       "      <th>INCOME_AMT_10000-24999</th>\n",
       "      <th>INCOME_AMT_100000-499999</th>\n",
       "      <th>INCOME_AMT_10M-50M</th>\n",
       "      <th>INCOME_AMT_1M-5M</th>\n",
       "      <th>INCOME_AMT_25000-99999</th>\n",
       "      <th>INCOME_AMT_50M+</th>\n",
       "      <th>INCOME_AMT_5M-10M</th>\n",
       "      <th>SPECIAL_CONSIDERATIONS_N</th>\n",
       "      <th>SPECIAL_CONSIDERATIONS_Y</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   APPLICATION_TYPE_Other  APPLICATION_TYPE_T10  APPLICATION_TYPE_T19  \\\n",
       "0                     0.0                   1.0                   0.0   \n",
       "1                     0.0                   0.0                   0.0   \n",
       "2                     0.0                   0.0                   0.0   \n",
       "3                     0.0                   0.0                   0.0   \n",
       "4                     0.0                   0.0                   0.0   \n",
       "\n",
       "   APPLICATION_TYPE_T3  APPLICATION_TYPE_T4  APPLICATION_TYPE_T5  \\\n",
       "0                  0.0                  0.0                  0.0   \n",
       "1                  1.0                  0.0                  0.0   \n",
       "2                  0.0                  0.0                  1.0   \n",
       "3                  1.0                  0.0                  0.0   \n",
       "4                  1.0                  0.0                  0.0   \n",
       "\n",
       "   APPLICATION_TYPE_T6  APPLICATION_TYPE_T7  APPLICATION_TYPE_T8  \\\n",
       "0                  0.0                  0.0                  0.0   \n",
       "1                  0.0                  0.0                  0.0   \n",
       "2                  0.0                  0.0                  0.0   \n",
       "3                  0.0                  0.0                  0.0   \n",
       "4                  0.0                  0.0                  0.0   \n",
       "\n",
       "   AFFILIATION_CompanySponsored  ...  INCOME_AMT_1-9999  \\\n",
       "0                           0.0  ...                0.0   \n",
       "1                           0.0  ...                1.0   \n",
       "2                           1.0  ...                0.0   \n",
       "3                           1.0  ...                0.0   \n",
       "4                           0.0  ...                0.0   \n",
       "\n",
       "   INCOME_AMT_10000-24999  INCOME_AMT_100000-499999  INCOME_AMT_10M-50M  \\\n",
       "0                     0.0                       0.0                 0.0   \n",
       "1                     0.0                       0.0                 0.0   \n",
       "2                     0.0                       0.0                 0.0   \n",
       "3                     1.0                       0.0                 0.0   \n",
       "4                     0.0                       1.0                 0.0   \n",
       "\n",
       "   INCOME_AMT_1M-5M  INCOME_AMT_25000-99999  INCOME_AMT_50M+  \\\n",
       "0               0.0                     0.0              0.0   \n",
       "1               0.0                     0.0              0.0   \n",
       "2               0.0                     0.0              0.0   \n",
       "3               0.0                     0.0              0.0   \n",
       "4               0.0                     0.0              0.0   \n",
       "\n",
       "   INCOME_AMT_5M-10M  SPECIAL_CONSIDERATIONS_N  SPECIAL_CONSIDERATIONS_Y  \n",
       "0                0.0                       1.0                       0.0  \n",
       "1                0.0                       1.0                       0.0  \n",
       "2                0.0                       1.0                       0.0  \n",
       "3                0.0                       1.0                       0.0  \n",
       "4                0.0                       1.0                       0.0  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a OneHotEncoder instance\n",
    "enc = OneHotEncoder(sparse=False)\n",
    "\n",
    "# Fit and transform the OneHotEncoder using the categorical variable list\n",
    "encode_df = pd.DataFrame(enc.fit_transform(application_df[application_cat]))\n",
    "\n",
    "# Add the encoded variable names to the dataframe\n",
    "encode_df.columns = enc.get_feature_names(application_cat)\n",
    "encode_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kimac\\AppData\\Local\\Temp/ipykernel_27768/2023052568.py:3: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
      "  application_df = application_df.drop(application_cat,1)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATUS</th>\n",
       "      <th>ASK_AMT</th>\n",
       "      <th>IS_SUCCESSFUL</th>\n",
       "      <th>APPLICATION_TYPE_Other</th>\n",
       "      <th>APPLICATION_TYPE_T10</th>\n",
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      ],
      "text/plain": [
       "   STATUS  ASK_AMT  IS_SUCCESSFUL  APPLICATION_TYPE_Other  \\\n",
       "0       1     5000              1                     0.0   \n",
       "1       1   108590              1                     0.0   \n",
       "2       1     5000              0                     0.0   \n",
       "3       1     6692              1                     0.0   \n",
       "4       1   142590              1                     0.0   \n",
       "\n",
       "   APPLICATION_TYPE_T10  APPLICATION_TYPE_T19  APPLICATION_TYPE_T3  \\\n",
       "0                   1.0                   0.0                  0.0   \n",
       "1                   0.0                   0.0                  1.0   \n",
       "2                   0.0                   0.0                  0.0   \n",
       "3                   0.0                   0.0                  1.0   \n",
       "4                   0.0                   0.0                  1.0   \n",
       "\n",
       "   APPLICATION_TYPE_T4  APPLICATION_TYPE_T5  APPLICATION_TYPE_T6  ...  \\\n",
       "0                  0.0                  0.0                  0.0  ...   \n",
       "1                  0.0                  0.0                  0.0  ...   \n",
       "2                  0.0                  1.0                  0.0  ...   \n",
       "3                  0.0                  0.0                  0.0  ...   \n",
       "4                  0.0                  0.0                  0.0  ...   \n",
       "\n",
       "   INCOME_AMT_1-9999  INCOME_AMT_10000-24999  INCOME_AMT_100000-499999  \\\n",
       "0                0.0                     0.0                       0.0   \n",
       "1                1.0                     0.0                       0.0   \n",
       "2                0.0                     0.0                       0.0   \n",
       "3                0.0                     1.0                       0.0   \n",
       "4                0.0                     0.0                       1.0   \n",
       "\n",
       "   INCOME_AMT_10M-50M  INCOME_AMT_1M-5M  INCOME_AMT_25000-99999  \\\n",
       "0                 0.0               0.0                     0.0   \n",
       "1                 0.0               0.0                     0.0   \n",
       "2                 0.0               0.0                     0.0   \n",
       "3                 0.0               0.0                     0.0   \n",
       "4                 0.0               0.0                     0.0   \n",
       "\n",
       "   INCOME_AMT_50M+  INCOME_AMT_5M-10M  SPECIAL_CONSIDERATIONS_N  \\\n",
       "0              0.0                0.0                       1.0   \n",
       "1              0.0                0.0                       1.0   \n",
       "2              0.0                0.0                       1.0   \n",
       "3              0.0                0.0                       1.0   \n",
       "4              0.0                0.0                       1.0   \n",
       "\n",
       "   SPECIAL_CONSIDERATIONS_Y  \n",
       "0                       0.0  \n",
       "1                       0.0  \n",
       "2                       0.0  \n",
       "3                       0.0  \n",
       "4                       0.0  \n",
       "\n",
       "[5 rows x 44 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge one-hot encoded features and drop the originals\n",
    "application_df = application_df.merge(encode_df,left_index=True, right_index=True)\n",
    "application_df = application_df.drop(application_cat,1)\n",
    "application_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kimac\\AppData\\Local\\Temp/ipykernel_27768/3766499242.py:3: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
      "  X = application_df.drop([\"IS_SUCCESSFUL\"],1).values\n"
     ]
    }
   ],
   "source": [
    "# Split our preprocessed data into our features and target arrays\n",
    "y = application_df[\"IS_SUCCESSFUL\"].values\n",
    "X = application_df.drop([\"IS_SUCCESSFUL\"],1).values\n",
    "\n",
    "# Split the preprocessed data into a training and testing dataset\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=78)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a StandardScaler instances\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# Fit the StandardScaler\n",
    "X_scaler = scaler.fit(X_train)\n",
    "\n",
    "# Scale the data\n",
    "X_train_scaled = X_scaler.transform(X_train)\n",
    "X_test_scaled = X_scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deliverable 2: Compile, Train and Evaluate the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 80)                3520      \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 30)                2430      \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 1)                 31        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 5,981\n",
      "Trainable params: 5,981\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Define the model - deep neural net, i.e., the number of input features and hidden nodes for each layer.\n",
    "number_input_features = len(X_train[0])\n",
    "hidden_nodes_layer1 =  80\n",
    "hidden_nodes_layer2 = 30\n",
    "\n",
    "nn = tf.keras.models.Sequential()\n",
    "\n",
    "# First hidden layer\n",
    "nn.add(\n",
    "    tf.keras.layers.Dense(units=hidden_nodes_layer1, input_dim=number_input_features, activation=\"relu\")\n",
    ")\n",
    "\n",
    "# Second hidden layer\n",
    "nn.add(tf.keras.layers.Dense(units=hidden_nodes_layer2, activation=\"relu\"))\n",
    "\n",
    "# Output layer\n",
    "nn.add(tf.keras.layers.Dense(units=1, activation=\"sigmoid\"))\n",
    "\n",
    "# Check the structure of the model\n",
    "nn.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import checkpoint dependencies\n",
    "import os\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint\n",
    "\n",
    "# Define the checkpoint path and filenames\n",
    "os.makedirs(\"checkpoints/\",exist_ok=True)\n",
    "checkpoint_path = \"checkpoints/weights.{epoch:02d}.hdf5\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile the model\n",
    "nn.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a callback that saves the model's weights every epoch\n",
    "cp_callback = ModelCheckpoint(\n",
    "    filepath=checkpoint_path, \n",
    "    verbose=1, \n",
    "    save_weights_only=True,\n",
    "    save_freq='epoch')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "803/804 [============================>.] - ETA: 0s - loss: 0.5704 - accuracy: 0.7206\n",
      "Epoch 00001: saving model to checkpoints\\weights.01.hdf5\n",
      "804/804 [==============================] - 1s 1ms/step - loss: 0.5703 - accuracy: 0.7208\n",
      "Epoch 2/100\n",
      "794/804 [============================>.] - ETA: 0s - loss: 0.5551 - accuracy: 0.7306\n",
      "Epoch 00002: saving model to checkpoints\\weights.02.hdf5\n",
      "804/804 [==============================] - 1s 1ms/step - loss: 0.5552 - accuracy: 0.7305\n",
      "Epoch 3/100\n",
      "793/804 [============================>.] - ETA: 0s - loss: 0.5521 - accuracy: 0.7323 ETA: 0s - loss: 0.548\n",
      "Epoch 00003: saving model to checkpoints\\weights.03.hdf5\n",
      "804/804 [==============================] - 1s 1ms/step - loss: 0.5521 - accuracy: 0.7322\n",
      "Epoch 4/100\n",
      "784/804 [============================>.] - ETA: 0s - loss: 0.5511 - accuracy: 0.7319\n",
      "Epoch 00004: saving model to checkpoints\\weights.04.hdf5\n",
      "804/804 [==============================] - 1s 1ms/step - loss: 0.5508 - accuracy: 0.7326\n",
      "Epoch 5/100\n",
      "779/804 [============================>.] - ETA: 0s - loss: 0.5502 - accuracy: 0.7326 ETA\n",
      "Epoch 00005: saving model to checkpoints\\weights.05.hdf5\n",
      "804/804 [==============================] - 1s 1ms/step - loss: 0.5499 - accuracy: 0.7325\n",
      "Epoch 6/100\n",
      "801/804 [============================>.] - ETA: 0s - loss: 0.5488 - accuracy: 0.7333\n",
      "Epoch 00006: saving model to checkpoints\\weights.06.hdf5\n",
      "804/804 [==============================] - 1s 1ms/step - loss: 0.5491 - accuracy: 0.7330\n",
      "Epoch 7/100\n",
      "780/804 [============================>.] - ETA: 0s - loss: 0.5479 - accuracy: 0.7335\n",
      "Epoch 00007: saving model to checkpoints\\weights.07.hdf5\n",
      "804/804 [==============================] - 1s 1ms/step - loss: 0.5477 - accuracy: 0.7333\n",
      "Epoch 8/100\n",
      "774/804 [===========================>..] - ETA: 0s - loss: 0.5476 - accuracy: 0.7336\n",
      "Epoch 00008: saving model to checkpoints\\weights.08.hdf5\n",
      "804/804 [==============================] - 1s 1ms/step - loss: 0.5479 - accuracy: 0.7336\n",
      "Epoch 9/100\n",
      "772/804 [===========================>..] - ETA: 0s - loss: 0.5463 - accuracy: 0.7366\n",
      "Epoch 00009: saving model to checkpoints\\weights.09.hdf5\n",
      "804/804 [==============================] - 1s 1ms/step - loss: 0.5468 - accuracy: 0.7358\n",
      "Epoch 10/100\n",
      "800/804 [============================>.] - ETA: 0s - loss: 0.5470 - accuracy: 0.7342 ETA: 0s - loss: 0.5489 - ac\n",
      "Epoch 00010: saving model to checkpoints\\weights.10.hdf5\n",
      "804/804 [==============================] - 1s 1ms/step - loss: 0.5469 - accuracy: 0.7342\n",
      "Epoch 11/100\n",
      "772/804 [===========================>..] - ETA: 0s - loss: 0.5445 - accuracy: 0.7358 ETA: 0s - loss: 0.547\n",
      "Epoch 00011: saving model to checkpoints\\weights.11.hdf5\n",
      "804/804 [==============================] - 1s 1ms/step - loss: 0.5461 - accuracy: 0.7347\n",
      "Epoch 12/100\n",
      "776/804 [===========================>..] - ETA: 0s - loss: 0.5449 - accuracy: 0.7347 ETA: 0s - loss: 0.5409 - accuracy\n",
      "Epoch 00012: saving model to checkpoints\\weights.12.hdf5\n",
      "804/804 [==============================] - 1s 1ms/step - loss: 0.5451 - accuracy: 0.7349\n",
      "Epoch 13/100\n",
      "784/804 [============================>.] - ETA: 0s - loss: 0.5447 - accuracy: 0.7364\n",
      "Epoch 00013: saving model to checkpoints\\weights.13.hdf5\n",
      "804/804 [==============================] - 1s 1ms/step - loss: 0.5449 - accuracy: 0.7363\n",
      "Epoch 14/100\n",
      "803/804 [============================>.] - ETA: 0s - loss: 0.5449 - accuracy: 0.7360 ETA: 0s - loss: 0.5439 - accura\n",
      "Epoch 00014: saving model to checkpoints\\weights.14.hdf5\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 65/100\n",
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      "804/804 [==============================] - 1s 1ms/step - loss: 0.5367 - accuracy: 0.7407\n",
      "Epoch 87/100\n",
      "797/804 [============================>.] - ETA: 0s - loss: 0.5363 - accuracy: 0.7411\n",
      "Epoch 00087: saving model to checkpoints\\weights.87.hdf5\n",
      "804/804 [==============================] - 1s 1ms/step - loss: 0.5365 - accuracy: 0.7407\n",
      "Epoch 88/100\n",
      "767/804 [===========================>..] - ETA: 0s - loss: 0.5363 - accuracy: 0.7393 ETA: 0s - loss: 0.5361 - accuracy: 0. - ETA: 0s - loss: 0.5350 - ac\n",
      "Epoch 00088: saving model to checkpoints\\weights.88.hdf5\n",
      "804/804 [==============================] - 1s 1ms/step - loss: 0.5366 - accuracy: 0.7395\n",
      "Epoch 89/100\n",
      "776/804 [===========================>..] - ETA: 0s - loss: 0.5368 - accuracy: 0.7404\n",
      "Epoch 00089: saving model to checkpoints\\weights.89.hdf5\n",
      "804/804 [==============================] - 1s 1ms/step - loss: 0.5363 - accuracy: 0.7409\n",
      "Epoch 90/100\n",
      "784/804 [============================>.] - ETA: 0s - loss: 0.5360 - accuracy: 0.7406\n",
      "Epoch 00090: saving model to checkpoints\\weights.90.hdf5\n",
      "804/804 [==============================] - 1s 1ms/step - loss: 0.5366 - accuracy: 0.7404\n",
      "Epoch 91/100\n",
      "784/804 [============================>.] - ETA: 0s - loss: 0.5356 - accuracy: 0.7411\n",
      "Epoch 00091: saving model to checkpoints\\weights.91.hdf5\n",
      "804/804 [==============================] - 1s 1ms/step - loss: 0.5361 - accuracy: 0.7406\n",
      "Epoch 92/100\n",
      "763/804 [===========================>..] - ETA: 0s - loss: 0.5342 - accuracy: 0.7414\n",
      "Epoch 00092: saving model to checkpoints\\weights.92.hdf5\n",
      "804/804 [==============================] - 1s 1ms/step - loss: 0.5361 - accuracy: 0.7403\n",
      "Epoch 93/100\n",
      "791/804 [============================>.] - ETA: 0s - loss: 0.5361 - accuracy: 0.7412\n",
      "Epoch 00093: saving model to checkpoints\\weights.93.hdf5\n",
      "804/804 [==============================] - 1s 1ms/step - loss: 0.5361 - accuracy: 0.7413\n",
      "Epoch 94/100\n",
      "783/804 [============================>.] - ETA: 0s - loss: 0.5364 - accuracy: 0.7403 ETA: 0s - loss: 0.5330 - \n",
      "Epoch 00094: saving model to checkpoints\\weights.94.hdf5\n",
      "804/804 [==============================] - 1s 1ms/step - loss: 0.5360 - accuracy: 0.7404\n",
      "Epoch 95/100\n",
      "766/804 [===========================>..] - ETA: 0s - loss: 0.5366 - accuracy: 0.7402\n",
      "Epoch 00095: saving model to checkpoints\\weights.95.hdf5\n",
      "804/804 [==============================] - 1s 1ms/step - loss: 0.5362 - accuracy: 0.7406\n",
      "Epoch 96/100\n",
      "761/804 [===========================>..] - ETA: 0s - loss: 0.5373 - accuracy: 0.7402 ETA: 0s - loss: 0.5371 - ac\n",
      "Epoch 00096: saving model to checkpoints\\weights.96.hdf5\n",
      "804/804 [==============================] - 1s 1ms/step - loss: 0.5363 - accuracy: 0.7414\n",
      "Epoch 97/100\n",
      "781/804 [============================>.] - ETA: 0s - loss: 0.5354 - accuracy: 0.7414\n",
      "Epoch 00097: saving model to checkpoints\\weights.97.hdf5\n",
      "804/804 [==============================] - 1s 1ms/step - loss: 0.5359 - accuracy: 0.7409\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 98/100\n",
      "785/804 [============================>.] - ETA: 0s - loss: 0.5355 - accuracy: 0.7412\n",
      "Epoch 00098: saving model to checkpoints\\weights.98.hdf5\n",
      "804/804 [==============================] - 1s 1ms/step - loss: 0.5360 - accuracy: 0.7407\n",
      "Epoch 99/100\n",
      "804/804 [==============================] - ETA: 0s - loss: 0.5361 - accuracy: 0.7403\n",
      "Epoch 00099: saving model to checkpoints\\weights.99.hdf5\n",
      "804/804 [==============================] - 1s 1ms/step - loss: 0.5361 - accuracy: 0.7403\n",
      "Epoch 100/100\n",
      "797/804 [============================>.] - ETA: 0s - loss: 0.5353 - accuracy: 0.7409\n",
      "Epoch 00100: saving model to checkpoints\\weights.100.hdf5\n",
      "804/804 [==============================] - 1s 1ms/step - loss: 0.5358 - accuracy: 0.7406\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "fit_model = nn.fit(X_train_scaled, y_train, epochs=100, callbacks=[cp_callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "268/268 - 0s - loss: 0.5562 - accuracy: 0.7266 - 315ms/epoch - 1ms/step\n",
      "Loss: 0.5561677813529968, Accuracy: 0.7266472578048706\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model using the test data\n",
    "model_loss, model_accuracy = nn.evaluate(X_test_scaled,y_test,verbose=2)\n",
    "print(f\"Loss: {model_loss}, Accuracy: {model_accuracy}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export our model to HDF5 file\n",
    "nn.save(\"AlphabetSoupCharity.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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